Nonlinear system identification for optimization of wireless power transfer

ABSTRACT

A method of detecting whether a receiver coil is near a transmit coil in a wireless power transfer system (WPTS), the method involving: applying a pseudo-random signal to the transmit coil; while the pseudo-random signal is being applied to the transmit coil, recording one or more signals produced within the WPTS in response to the applied pseudo-random signal; by using the one or more recorded signals, generating a dynamic system model for some aspect of the WPTS; and using the generated dynamic system model in combination with stored training data to determine whether an object having characteristics distinguishing the object as a receiver coil is near the transmit coil.

This application claims the benefit of U.S. Provisional Application No.61/738,786, filed Dec. 18, 2012, all of which is incorporated herein byreference.

TECHNICAL FIELD

Embodiments of the present invention generally relate to wireless powertransfer for charging and/or powering systems such as might be found in,but without limitation, electric vehicles and portable devices.

BACKGROUND OF THE INVENTION

With the renewed interest in electric cars we have seen a number of newdevelopments in battery technology, fast charging techniques andwireless power transmission as a convenient method to rechargebatteries. Wireless fast charging techniques become even more relevantfor pure electric cars as a method to alleviate the limited rangeprovided by current battery technology. In this way batteries could berecharged either while driving from coils embedded in the roads, attraffic lights, in parking lots while shopping or at drive-ins.

Wireless power transfer has a long history starting probably with Tesla.The technology is now used everywhere, from toothbrushes, cell phones,notebooks and is even considered for general use in houses such aslamps, clocks, etc. In most applications wireless power transfer is usedfor charging batteries, which is used as a temporary energy reservoirbetween the wireless charging system and the device. With the advent ofbetter battery technologies, such as lithium-ion cells, it becomesfeasible to charge batteries much more rapidly than before and to do sowith wireless fast chargers. To achieve general acceptance, thesewireless fast chargers need to be efficient and robust, which is thefocus of some of the applications discussed in this document.

There are many types of wireless power transfer. This disclosure focuseson Resonant Induction Charging (RIC), although much of what is describedalso applies to other types of wireless charging methods. RIC, as thename implies, uses high-Q tuned coils and capacitors, and power istransmitted from coil to coil through magnetic fields. RIC differs fromfar-field techniques involving, for example, very high frequency RFfields, which require sophisticated electronics, and near-fieldtechniques, which only work within a fraction of a wavelength when usingRIC. With RIC, it is found that significantly more power can betransferred between coils and up to a distance exceeding several coildiameters. Using a magnetic field rather than a radiatingelectromagnetic field also presents fewer potential health hazards.

A common type of coil used for RIC is a pancake coil with a singlespiral winding arrayed in a plane. The circuit diagram in FIG. 1 shows atypical circuit used for RIC, where coils L1 and L2 would be thetransmit and receiver coils, respectively, fabricated as pancake coils.As it is the case for transformers, the electrical characteristics ofthe coils can be described by the coils' resistances, self-inductance,and mutual inductance. The mutual inductance is related to how much ofthe field generated by one coil traverses the other coil(s), which islargely related to the geometry of how the coils are orientated withrespect to each other, including distance and orientation. As thecoupling decreases, less of the power is transmitted while the powerloss in joule heating remains the same or increases, and hence theefficiency decreases.

SUMMARY OF THE INVENTIONS

In general, in one aspect, at least one of the inventions features amethod of detecting whether a receiver coil is near a transmit coil in awireless power transfer system (WPTS). The method involves: applying apseudo-random signal to the transmit coil; while the pseudo-randomsignal is being applied to the transmit coil, recording one or moresignals produced within the WPTS in response to the appliedpseudo-random signal; by using the one or more recorded signals,generating a dynamic system model for some aspect of the WPTS; and usingthe generated dynamic system model in combination with stored trainingdata to determine whether an object having characteristicsdistinguishing the object as a receiver coil is near the transmit coil.

Other embodiments include one or more of the following features. Themethod also includes, if the receiver coil is determined to be near thetransmit coil, initiating a wireless power transfer through the transmitcoil to the receiver coil. The pseudo-random signal is a pseudo-randomvoltage signal and/or is sufficiently strong to stimulate nonlinearitiesin a receiver system connected to the receiver coil. The one or moresignals includes a current signal of the transmit coil and possibly avoltage signal of the transmit coil. Using the generated dynamic systemmodel involves comparing information contained in the generated dynamicsystem model to empirically-derived, stored information that isindicative of a nearby presence of a receiver coil. Generating thedynamic system model involves using system identification or nonlinearsystem identification to fit a selected model to data derived from theone or more recorded signals. The selected model is a Wiener systemand/or the selected model has a dynamic linear part and a staticnonlinear part. The dynamic system model is an impedance function forthe transmit coil or a transmitted power function for transmit coil.Using the generated dynamic system model involves decomposing thedynamic system model into basis functions to generate a set of basisfunction parameters and using the set of basis function parameters todetermine whether a receiver coil is near the transmit coil. Thepseudo-random signal is a selected one of a Gaussian White Noise signaland a Pseudo-Random Binary Sequence (PRBS). The generated dynamic systemmodel includes a time domain representation or a frequency domainrepresentation.

Still other embodiments include one or more of the following features.The stored training data is represented by a stored filter function andwherein using the generated dynamic system model in combination withstored training data comprises processing the generated dynamic systemmodel to generate an output signal, wherein the output signal indicateswhether an object having characteristics recognizable from the storedtraining data as a receiver coil is near the transmit coil and whereinprocessing the dynamic system model comprises applying the stored filterfunction. Generating the dynamic system model involves computing afrequency spectrum from the one or more recorded signals. The generateddynamic system model is an impedance spectrum for the transmit coil. Thefilter function is a nonlinear filter function. The nonlinear filterfunction was derived from measurements made on a test system including atest transmit coil and a test receiver coil located at differentdistances of separation from each other. The method also involves, if areceiver coil is detected near the transmit coil, initiating a wirelesspower transfer through the transmit coil to the detected receiver coil.

In general, in yet another aspect, at least one invention features awireless power transfer system. The wireless power transfer systemincludes: a transmit coil; a power transmitter circuit connected to thetransmit coil; a sensor circuit connected to the transmit coil; and acontroller for controlling the power transmitter circuit and the sensorcircuit, wherein the controller includes a memory for storing trainingdata and a processor system programmed to: cause the power transmittercircuit to apply a pseudo-random signal to the transmit coil; while thepseudo-random signal is being applied to the transmit coil, cause thesensor circuit to record one or more signals produced the within theWPTS in response to the applied pseudo-random signal; by using the oneor more recorded signals, generate a dynamic system model for someaspect of the WPTS; and use the generated dynamic system model incombination with the stored training data to determine whether an objecthaving characteristics distinguishing the object as a receiver coil isnear the transmit coil.

Other embodiments of the invention include one or more of the followingfeatures. The one or more signals includes a current signal and avoltage signal of the transmit coil. The stored training data isrepresented by a stored filter function and the processor system isprogrammed to use the generated dynamic system model in combination withstored training data by processing the generated dynamic system model togenerate an output signal, wherein the output signal indicates whetheran object having characteristics recognizable from the stored trainingdata as a receiver coil is near the transmit coil and the processing ofhe generated dynamic system model involves applying the filter function.The processor system is programmed to generate the dynamic system modelby computing a frequency spectrum from the one or more recorded signals.The generated dynamic system model is an impedance spectrum for thetransmit coil.

In general, in still yet another aspect, at least one invention featuresa method of finding an operating frequency for a drive signal to atransmit coil in a wireless power transfer system (WPTS). The methodinvolves: applying a pseudo-random signal to the transmit coil; whilethe pseudo-random signal is being applied to the transmit coil,recording one or more signals produced within the WPTS in response tothe applied pseudo-random signal; by using the one or more recordedsignals, generating a dynamic system model for some aspect of the WPTS;and conducting a search for an optimum frequency for the drive signal,wherein conducting the search comprises repeatedly using the generateddynamic system model to simulate a response to the drive signal whilevarying the operating frequency of the drive signal until the optimumfrequency is found.

Other embodiments include one or more of the following features.Conducting a search involves: computing an output power from thesimulated response; using the computed output power as an objectivefunction; and conducting the search by using the objective function. Thealso involves setting the operating frequency of the drive signal to theoptimum frequency. The pseudo-random signal is a pseudo-random voltagesignal. The one or more signals includes a current signal of thetransmit coil or it includes both a current signal and a voltage signalof the transmit coil. Generating the representation of the transferfunction involves using system identification or nonlinear systemidentification to fit a model to data derived from the one or morerecorded signals. The selected model is a Wiener system or it has adynamic linear part and a static nonlinear part. The generated dynamicsystem model includes a time domain representation or a frequency domainrepresentation.

In general, in still another aspect, at least one of the inventionsfeatures a wireless power transfer system. The wireless power transfersystem includes: a transmit coil; a power transmitter circuit connectedto the transmit coil; a sensor circuit connected to the transmit coil;and a controller for controlling the power transmitter circuit and thesensor circuit, wherein said controller includes a memory for storingtraining data and a processor system programmed to: cause the powertransmitter to apply a pseudo-random signal to the transmit coil; whilethe pseudo-random signal is being applied to the transmit coil, causethe sensor circuit to record one or more signals produced within theWPTS in response to the applied pseudo-random signal; by using the oneor more recorded signals, generate a dynamic system model for someaspect of the WPTS; and conduct a search for an optimum frequency forthe drive signal, wherein conducting the search comprises repeatedlyusing the generated dynamic system model to simulate a response to thedrive signal while varying the operating frequency of the drive signaluntil the optimum frequency is found.

In general, in another aspect, at least one of the inventions features amethod of finding an operating frequency for a drive signal to atransmit coil in a wireless power transfer system (WPTS). The methodinvolves: applying a pseudo-random signal to the transmit coil; whilethe pseudo-random signal is being applied to the transmit coil,recording a signal produced within the WPTS in response to the appliedpseudo-random signal; and processing the recorded signal to generate anoutput signal, wherein the output signal identifies the operatingfrequency to be used for the drive signal and wherein processing therecorded signal comprises applying a nonlinear filter function.

Other embodiments include one or more of the following features. Thenonlinear filter function was derived from measurements made on a testsystem including a test transmit coil and a test receiver coil locatedat different distances of separation from each other. Recording a signalproduced within the WPTS in response to the applied pseudo-random signalinvolves recording a signal produced by the transmit coil.

In general, in yet another aspect, at least one of the inventionsfeatures a wireless power transfer system (WPTS). The wireless powertransfer system includes: a transmit coil; a power transmitter circuitconnected to the transmit coil; a sensor circuit connected to thetransmit coil; and a controller for controlling the power transmittercircuit and the sensor circuit, wherein said controller includes amemory for storing a nonlinear filter function and a processor systemprogrammed to: cause the power transmitter to apply a pseudo-randomsignal to the transmit coil; while the pseudo-random signal is beingapplied to the transmit coil, cause the sensor circuit to record asignal produced within the WPTS in response to the applied pseudo-randomsignal; and process the recorded signal to generate an output signal,wherein the output signal identifies the operating frequency to be usedfor the drive signal and wherein processing the recorded signal involvesapplying a nonlinear filter function.

Other embodiments include one or more of the following features. Thenonlinear filter function was derived from measurements made on a testsystem including a test transmit coil and a test receiver coil locatedat different distances of separation from each other. The signalproduced within the WPTS in response to the applied pseudo-random signalis a signal produced by the transmit coil.

In general, in still yet another aspect, at least one of the inventionsfeatures a method of identifying a waveform for a drive signal to atransmit coil in a wireless power transfer system (WPTS). The methodinvolves: applying a pseudo-random signal to the transmit coil; whilethe pseudo-random signal is being applied to the transmit coil,recording one or more signals produced within the WPTS in response tothe applied pseudo-random signal; by using the one or more recordedsignals, generating a dynamic system model for some aspect of the WPTS;and conducting a search for an optimum waveform for the drive signal,wherein conducting the search comprises repeatedly using the generateddynamic system model to simulate a response to the drive signal whilevarying the waveform of the drive signal until the optimum waveform isfound.

Other embodiments include one or more of the following features.Conducting a search further involves: computing an output power from thesimulated response; using the computed output power as an objectivefunction; and conducting the search by using the objective function. Theone or more signals produced within the WPTS includes a signal producedby the transmit coil in response to the applied pseudo-random signal.

In general, in another aspect, at least one of the inventions features awireless power transfer system. The wireless power transfer systemincludes: a transmit coil; a power transmitter circuit connected to thetransmit coil; a sensor circuit connected to the transmit coil; acontroller for controlling the power transmitter circuit and the sensorcircuit, wherein the controller includes a memory for storing anonlinear filter function and a processor system programmed to: causethe power transmitter to apply a pseudo-random signal to the transmitcoil; while the pseudo-random signal is being applied to the transmitcoil, cause the sensor circuit to record one or more signals producedwithin the WPTS in response to the applied pseudo-random signal; byusing the one or more recorded signals, generate a dynamic system modelfor some aspect of the WPTS; and conduct a search for an optimumwaveform for the drive signal by repeatedly using the generated dynamicsystem model to simulate a response to the drive signal while varyingthe waveform of the drive signal until the optimum waveform is found.

The details of one or more embodiments of the invention are set forth inthe accompanying drawings and the description below. Other features,objects, and advantages of the invention will be apparent from thedescription and drawings, and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts a power transfer circuit with a transmit coil L₁ and areceiver coil L₂.

FIG. 2 presents the typical electrical impedance of a tuned coil in freespace and in connection with a neighboring identical coil.

FIG. 3 shows the transmitter coil impedance spectra for two coils spacedapart from each other by differing amounts.

FIG. 4 shows the transmitter coil power spectrum corresponding to thecoil impedance spectra of FIG. 3.

FIG. 5 presents the optimal coil frequency for two coils versus theirseparation distance.

FIG. 6 depicts the block diagram of a Wiener system.

FIG. 7 depicts the block diagram of a Hammerstein system.

FIG. 8 is a block diagram of a wireless power transfer system includingtransmit and receiver coils.

FIG. 9 presents a flow chart of an algorithm for pre-training atransmitter power controller of a wireless power transfer system fordetecting a wireless power receiver system.

FIG. 10 presents a flow chart of an algorithm implemented by thetransmitter power controller to detect the presence of a wireless powerreceiver system.

FIG. 11 presents a flow chart of an algorithm for creating a nonlinearfilter for use by a transmitter power controller to detect the presenceof a wireless power receiver system.

FIG. 12 presents a flow chart of an algorithm implemented by thetransmitter power controller for using the nonlinear filter of FIG. 11to detect the presence of a wireless power receiver system.

FIG. 13 presents a flow chart of an algorithm implemented by thetransmitter power controller to auto-adjust the frequency of thewireless power transfer signal.

FIG. 14A presents a flow chart of an algorithm for creating a nonlinearfilter for use by a transmitter power controller to auto-adjust thefrequency of the wireless power transfer signal.

FIG. 14B presents a flow chart of an algorithm implemented by thetransmitter power controller for using the nonlinear filter of FIG. 14Ato auto-adjust the frequency of the wireless power transfer signal.

FIG. 15 presents a flow chart of an algorithm implemented at least inpart by the transmitter power controller to adjust the waveform of thewireless power signal.

FIGS. 16A-B present a flow chart of an algorithm for pre-training thetransmitter power controller of a wireless power transfer system fordetecting foreign objects.

FIG. 17 presents a flow chart of an algorithm implemented by thetransmitter power controller to detect foreign objects.

FIG. 18A presents a flow chart of an algorithm for creating a nonlinearfilter for use by a transmitter power controller to detect the presenceof a foreign object.

FIG. 18B presents a flow chart of an algorithm implemented by thetransmitter power controller for using the nonlinear filter of FIG. 18Ato detect the presence of a foreign object.

DETAILED DESCRIPTION

Before presenting the details of the different embodiments, some of theissues that are being addressed by those embodiments will first bediscussed.

FIG. 2 gives a typical electrical impedance of a tuned coil, which may,for example, correspond to a transmit coil in a wireless power transfersystem. The impedance of an ideal capacitor in series with an inductorhas a “zero” null at the resonant or natural frequency, as shown by thecurve C. When a second tuned coil (an inductor and capacitor inparallel), referred to as a receiver coil, is brought in close proximityto the transmit coil, the impedance at the natural frequency increasessignificantly as shown by curve A. The curve labeled B shows theimpedance of the two-coil system when the receiver coil also includes aresistive load in the circuit to dissipate the power generated in theexternal loop. As can be appreciated, an impedance measurement, whichonly requires access to the two terminals of the transmit coil, couldprovide a very convenient tool for gaining insight into the operation ofthe circuit.

As illustrated by curve A in FIG. 2, a more important phenomenon thatoccurs when the two coils are brought into close proximity to each otheris frequency splitting. When the transmit coil and receiver coil come inclose range, two or more frequencies can be observed where locally inthe frequency domain the power transfer is optimal. In other words, oneobserves two minima, one on each side of the natural frequency. (Forfurther discussion of this phenomenon see: Alanson P. Sample and JoshuaR. Smith, Analysis, Experimental Results, and Range Adaptation ofMagnetically Coupled Resonators for Wireless Power Transfer, 2010 IEEE;and Huang, X. L., et al., Resonant Frequency Splitting Analysis andOptimization of Wireless Power Transfer System, PIERS Proceedings,Russia, Aug. 19-23, 2012.)

FIG. 3 gives the typical impedance of a transmit coil at severaldistances from the receiver coil. As the coils get progressively closerto each other, the peak in the impedance at the natural frequencyincreases and the two frequencies at which the impedance minimums occuron either side of that location spread further apart. From thoseimpedance spectra, power spectra indicating how much power is going intothe coil can be calculated. The corresponding power spectra are shown inFIG. 4. As clearly indicated, the power is maximal at two frequencieswhose separation becomes smaller as a function of the separation of thetwo coils, until the two peaks merge for all practical purposes atlonger separations (merge at natural frequency of system).

FIG. 5 presents for a different wireless system the optimal frequency atwhich the maximum power transfer occurs versus the relative separationdistance of the two coils in terms of coil diameters. As can beobserved, when the distance between the coils is less than about halfthe diameter of the coils, there are two frequencies at which a maximumpower transfer is observed. In a fixed setup, it is easy to select anoptimal oscillator frequency to transfer power. In a dynamic situation,however, when coils move relative to each other and the distance betweenthe coils is unknown, it becomes a challenge to maintain optimal powertransfer.

Normally, the objective is to maximize the power transfer to the load.In a laboratory environment, it is possible to connect sense leads tomeasure power generated by the power circuitry in the transmitter and tomeasure power received by the load. It is then possible to sweep thoughall frequencies and periodically measure the ratio of received overtransmitted power, find the frequency at which the peak power transferoccurs, and adjust the oscillator frequency accordingly. Finding theoptimal frequency can be done using a number of algorithms developed inoptimization theory. However, due to frequency splitting and thepossible existence of two local optimum frequencies, techniquesinvolving stochastic minimization should be used. When an optimum isfound, the optimum frequency can be tracked in real time more rapidlythrough local searches.

In real life applications, however, such as cars moving relative to eachother or over transmit coils, it may not be possible to directly measurethe power dissipated in the load. Automatic frequency tuning can beachieved through directional couplers located, for example, between thetransmit and receiver coils to measure the incident and reflected power(see, Sample and Smith). Another technique might involve wirelesslytransmitting the required measurements, such as load current, voltage,and power, from the receiver to the transmitter. This could be donethrough sending a modulated signal from the receiver coil to thetransmit coil by using a different frequency band that is not affectedby the power transmit signal. Alternatively, other transmitting mediacould be used to send the information back to the transmitter, such asoptical or acoustic signals. Secondary coils could be embedded in thepower coils in order to transmit such signals.

As shown by Sample and Smith, modeling techniques can be used to relatetransmitted power to coil position and orientation. In this case,position sensors that give the distance and orientation between the twocoils can be used to identify the optimal power frequency.

The power transfer system shown in FIG. 1 includes severalnonlinearities, such as in the rectifiers and in the secondaryconverter. If the system was linear, a number of techniques described inthe engineering literature could be used to quickly identify the systemand from there extract optimum parameters. However, if such techniqueswere to be used to find how perturbations in the transmit signal wouldaffect the signal observed in the load, they would probably fail or giveinaccurate descriptions due to the presence of hard nonlinear elements,such as rectifier diodes, in the circuit.

It also has been found that increased power transfer can be achievedusing a non-sinusoidal transient waveform. This is of particular benefitsince switching power modules or power FETs are used to minimize powerlosses in the electronics, and these generate signals in the form ofpulses or steps rather than sinusoidally. Such power modules ortransistors include IGBT (integrated gate bipolar transistors) andHEXFET® modules, IGBT's typically being capable of switching at highervoltages and HEXFETs being capable of switching at higher frequencies,e.g. up to tens of Mega Hertz in frequency.

Deriving a systematic method to determine the optimal wave shape of sucha signal in real-time while coils are translating and rotating withrespect to each other is one of the objectives achieved by the methodsdescribed below.

Nonlinear System Identification

At least some of the embodiments described herein employ nonlinearsystem identification to achieve the results that are achieved. So,before discussing the details of the various embodiments, a brief reviewof nonlinear system identification will first be presented.

As is well known from Fréchet's Theorem, any finite-memorytime-invariant nonlinear dynamic system can be represented witharbitrary precision with a finite order Volterra series for all inputsthat are square integrable over a finite interval. A Volterra series issimilar to a Taylor series, except it can capture “memory” effects ofdevices such as capacitors and inductors. A Volterra series, whichrepresents a functional expansion of a dynamic, nonlinear,time-invariant functional, is an infinite sum of multidimensionalconvolutional integrals of the following form:

${y(t)} = {K_{0} + {\sum\limits_{n = 1}^{+ \infty}{\int_{- \infty}^{+ \infty}\mspace{14mu} {\ldots \mspace{14mu} {\int_{- \infty}^{+ \infty}{{{K_{n}\left( {\tau_{1},\tau_{2},{\ldots \mspace{14mu} \tau_{n}}} \right)} \cdot {x\left( {t - \tau_{1}} \right)} \cdot {x\left( {t - \tau_{2}} \right)} \cdot \ldots \cdot {x\left( {t - \tau_{n}} \right)}}{\tau_{1}}{\tau_{2}}\mspace{14mu} \ldots \mspace{14mu} {\tau_{n}}}}}}}}$

Closely related to the Volterra series is the Wiener series. In theWiener series, the terms are orthogonalized for a purely random whitenoise input, and are more readily identified using, for example,cross-correlation techniques.

Korenberg (in Parallel Cascade Identification and Kernel Estimation forNonlinear Systems, Annals of Biomedical Engineering, vol. 19, pp. 429-55(1990) expanded the above-referenced Fréchet's theorem by proving thatany discrete-time finite-memory system that can be represented by afinite Volterra series can also be represented by a finite series ofparallel cascades of a dynamic linear system followed by a staticnonlinearity (i.e., by a cascade of Wiener systems or LN systems).

One example of a dynamic system model is the Wiener system, shown inFIG. 6. In such a system, a dynamic linear system represented by h(τ) isfollowed by a static nonlinear system represented by N(•). This is alsoreferred to as an LN system. The dynamic linear system must bestationary (time invariant), stable and memory-less. It maps all thepossible and acceptable set of input functions of time x(t) to an outputfunction of time u(t). The static nonlinearity maps the range ofacceptable real values “u(t)” to real values “y(t)” within the range ofthe function. These two components, the dynamic linear part and staticnonlinearity, can be represented either parametrically ornon-parametrically. Typically, a parametric representation involves sometype of symbolic expression involving parameters a₀, a₁, . . . a_(n).For example, a polynomial such as the following may be used to representa static nonlinearity:

${f(x)} = {a_{0} + {\sum\limits_{n = 1}^{N}\left( {a_{n}*x^{n}} \right)}}$

Wiener systems are instances of a class of models known as cascade orblock structured systems. Other instances of cascade or block structuredsystems include: Hammerstein systems, as shown in FIG. 7, in which adynamic linear system follows a static nonlinear system (NL); andcascade systems in which a linear system is followed by a nonlinearityand then by another linear system (LNL).

There are a number of system identification methods, some quite general,others being more specific and based on assumptions about properties ofthe input function applied to the system. They may also depend on thespace in which the model is used. One of the most general nonlinearsystem identification techniques involves expressing a function givingan error in the predicted model. Using a parametric representation ofthe system, a nonlinear minimization technique, such as theLevenberg-Marquardt technique can be used to find the parameters thatminimize the error function. This approach is general andstraightforward to implement but typically computationally inefficientcompared to other techniques.

In the case of the nonparametric form of the Wiener (LN) model,extremely efficient techniques have been developed by Korenberg andHunter. They have also developed efficient techniques for identifyingHammerstein (NL) systems. Such techniques are described in Hunter etal., The Identification of nonlinear Biological Systems: Wiener andHammerstein Cascade Models, Biological Cybernetics, vol. 55 pp. 135-44(1986). And they have developed practical and efficient techniques toidentify a parallel cascade of a linear-system followed by a staticnonlinearity and another linear system (LNL), as described, for example,in Korenberg et al., The Identification of Nonlinear Biological Systems:LNL Cascade Models, Biological Cybernetics, vol. 55, pp. 125-34, (1986).It has been demonstrated that every continuous discrete time system withfinite memory can be uniformly approximated by a finite sum of LNLsystems.

The nonparametric functions implemented numerically end up beingrepresented as sampled functions and involve a very large number ofnumerical values. Therefore, often these sampled data functions areconverted to a parametric form. In this way, the efficiency ofcomputation is preserved and the final representation ends up being moreparsimonious. In many cases, after inspection of the impulse responsethe order of the system may be inferred and after fitting a simplifiedreduced-order model the impulse response ends up being filtered and lessnoisy.

Additional explication of the use of nonlinear system identification,with respect, more particularly, to Wiener and Volterra kernels, mayalso be found in the following references: Korenberg, et al., ExactOrthogonal Kernel Estimation From Finite Data Records: ExtendingWiener's Identification Of Nonlinear Systems, Annals of BiomedicalEngineering, vol. 16, pp. 201-14 (1988); Korenberg, et al., TheIdentification of Nonlinear Biological Systems: Wiener KernelApproaches, Annals of Biomedical Engineering, vol. 18, pp. 629-54(1990); and Korenberg, et al., The Identification of NonlinearBiological Systems: Volterra Kernel Approaches, Annals of BiomedicalEngineering, vol. 24, pp. 250-68 (1996). Further details may also befound in U.S. Pat. Appln. Pub. No. 2012/0098481 entitled “Apparatus andMethod for Rapidly Charging Batteries” by Ian W. Hunter and Serge R.Lafontaine, the contents of which are incorporated herein by reference.

It should be noted that a system that has hysteresis lends itself to aparametric approach, whereas it does not lend itself to the use of theabove-mentioned Korenberg and Hunter fast identification methods foridentifying structured blocks. As noted above, if the parametricapproach is used, then the Levenberg-Marquardt technique can be used tofind the parameters that minimize the error function, e.g. thedifference between the predicted Wiener output and the real systemoutput.

It should be understood that the above-mentioned techniques can beemployed, where appropriate, to perform the nonlinear systemidentification discussed herein.

Application of Nonlinear System Identification to Wireless PowerTransfer

In embodiments described herein, nonlinear system identification usingthe techniques mentioned above is applied to improve wireless powertransfer and fast chargers in order to adjust the parameters of thepower signal fed to the transmit coil in order to: detect when areceiver coil is in close enough proximity to start transmitting power;adjust the frequency automatically as the receiver coil moves; adjustthe waveform of the signal used to transmit power; and detect when anobject interferes with the power transmission.

Various embodiments that implement these functions are described indetail below.

The Wireless Power Transfer System

Referring to FIG. 8, an example of a system in which the variousembodiments can be implemented includes a wireless power transmittersystem 10 and a receiver system 50. Depending on the desiredapplication, the transmitter system might be located on a platform 11,which might be a stationary platform or it might be a mobile platform,such as a vehicle or the wheel on a vehicle. The receiver system islocated on a mobile platform 51 (e.g. an electric vehicle or the wheelon a vehicle) that includes a chargeable battery module 56 for storingthe energy to operate equipment on the mobile platform, e.g. theelectric engine.

The transmitter system includes a transmit coil 12 through which poweris wirelessly transferred to a receiver system by way of resonantinductive charging (RIC). The transmitter system also includes a powertransmitter circuit 14 which drives transmit coil, a power transmittercontroller 16 which operates power transmitter circuit 14 and performsthe functions to be described below, a power supply system 18 forproviding power to transmit coil 12 and for powering the various otherelectrical components, and sensor and measurement circuitry 20 which iscapable of measuring and recording current and voltage signals attransmit coil 12.

The controller includes a processor system 24 (including one or moreprocessors) for running the algorithms that are described herein, forexecuting the code for operating the power transmitter circuit, and forperforming other functions associated with the power transmitter system.It also includes memory (RAM and ROM) 26 for storing code that isexecuted by processor system 24, including the code corresponding to thefunctionality of the algorithms described herein, and for storing datathat is used by processor system 24 and data that is generated byprocessor system 24 in the course of implementing the algorithmsdescribed herein. There is also a hard drive 28 connected to theprocessor system and to which processor system 24 has access. Itprovides computer-readable, digital storage for the programs which areloaded into active memory and which are run on the processor toimplement the algorithms described herein.

The receiver system 50 includes a receiver coil 52 through which itreceives power wirelessly transmitted through transmit coil 12. It alsoincludes a rechargeable battery module 56 (including for example lithiumion battery cells) and a battery management system 54 for managing theoperation of battery module 56. With regard to the embodiments describedherein, battery management system 54 is responsible for assisting in thecharging of battery module 56 when power is wirelessly received throughreceiver coil 52 from a neighboring power transmitter system.

Detecting the Presence of a Receiver System.

Typically, it is preferable to energize the transmit coils only when areceiver coil is in position to receive power. The presence of areceiver coil could be detected using a number of means, such as aproximity switch, RFID tags, a low power signal radiated from thereceiver coil to the transmit coil in a sideband, using acoustic oroptical transceivers, or an operator that pushes on a button. But evenif one of those techniques is used, there is still a need to confirmthat the detected receiver is in the proper location. There will be alsocases where it would be desirable to automatically detect the presenceof a legitimate coil and automatically start charging when the coil isin position.

As shown in FIG. 2, the impedance measured from the transmit coil goesthrough significant changes as a receiver coil moves towards thereceiver and this offers a means to detect a receiver coil. Theimpedance can be obtained by sweeping a pure sine wave signal over afrequency range and plotting the ratio of voltage to current as afunction of frequency. However, system identification offers a betterapproach. In systems theory, a dynamical system maps a domain ofadmissible time functions to a range of output time functions. A coilcan be considered as a system that is excited by a time-varying voltage(or current) and produces a time response in the form of a current (orvoltage). Standard non-parametric time-domain linear systemidentification techniques provide a system model as an impulse responsewhich can then be used to calculate the frequency domain systemresponse, which corresponds to the impedance in the case of the transmitcoil. Such techniques are described in the following publicly availablereferences: Eykhoff, P., System Identification: parameter and stateestimation, Wiley, London (1974); Goodwin, G. C., Payne, R. L., Dynamicsystem identification: experimental design and data analysis, AcademicPress, New York (1977); Graupe D, Identification of systems, VanNostrand Reinhold, NY (1976); and Ljung, System Identification—Theoryfor the User, 2nd Ed., PTR Prentice Hall, (1999).

As mentioned above, systems theory also provides a number of nonlinearsystem identification techniques providing nonlinear systemrepresentations such as the kernels of a Volterra series expansion orblocks of a structured block system representation.

In nonlinear system identification, the linear part, which correspondsto the usual admittance (impedance} measurements, is obtained in thetime domain in the form of an impulse response which can then be mappedin the frequency domain using well established techniques (e.g. DiscreteFourier Transform) to obtain the impedance spectra. The nonlinearcomponents, which are the higher order Kernels in the case of a Volterraseries or nonlinearities in the case of the block structured approach,give a signature of nonlinear components such as electronic components(rectifiers, etc.) present in the secondary coil. Hence, the merepresence of nonlinear components will provide information about thepresence of a system to charge or power. This, however, may not besufficient in cases where other systems including nonlinear components,usually in the form of other electronic components, might come in thevicinity of the transmitter.

In the case where it is known that all receivers have nearly identicalcharacteristics, a coil detection system can be pre-trained as shown inFIG. 9. A first receiver is used at different locations, and possiblytested for varying charge levels and ambient temperatures. A powersignal is applied to the transmit coil and nonlinear systemidentification used to obtain different dynamic system models orrepresentations thereof for each position and for other requiredparameters. These different models are kept in a database to define amodel parameter space. Then, as shown in FIG. 10, a detection phase isimplemented in which experiments are repeatedly performed that involveapplying a PR (pseudo-random) signal and using nonlinear systemidentification to model the resulting coil dynamics.

In order to parameterize the space of nonlinearities and impedancespectra, a set of optimal and orthogonal basis functions is determinedusing techniques such as Singular Value Decomposition (SVD), PrincipalComponent Analysis (PCA), wavelets, or splines. The model functionalsare decomposed into their principal components, and the coefficientsthus obtained, when exceeding a threshold, are used to indicate thepresence of a receiver coil. For further discussion of such techniques,refer to Chatterjee, Anindya, An introduction to the proper orthogonaldecomposition, Current Science, Vol. 78, No. 7, 2000.

The operation of the algorithms illustrated in FIGS. 9 and 10 will nowbe described in greater detail.

With reference to FIG. 9, the procedure for pre-training the transmitterpower controller begins with first defining a representative set ofsystem configurations for which tests will be performed (100). The testsare performed using systems that are identical to those on which thedetection will be performed in the field. The representative set ofconfigurations will include at least a range of locations andorientations of the receiver coil relative to the transmit coil. Inaddition, it might also include different values for one or moreoperational parameters that might be expected to impact on the detailsof the nonlinear model. Such operational parameters might include, forexample, the temperature of the receiver system and the state of chargeof the battery being charged by the receiver system. The selection oflocations and orientations is guided by the relative physicalrelationships that are considered to be relevant during operation of thepower transmitter in the field. For example, this might involveestablishing a maximum distance at which power transfer can begin totake place and then defining further closer distances by moving thereceiver coil toward the transmit coil in incremental steps. To build amore complete set of data, this could be repeated as the receiver coilis moved towards the transmit coil along different paths and fordifferent orientations of the receiver coil relative to the transmitcoil.

Using this defined set of locations and orientations, data is gatheredand processed for each of the defined configurations (102-114). Thisinvolves, for each configuration, driving the transmit coil of the powertransfer circuit with a pseudo-random voltage signal (e.g. GWN voltagesignal or PRBS) that includes sufficient power to stimulate thenonlinear elements in the wireless receiver system (102). While power isbeing applied to the transmit coil, the voltage and current at thetransmit coil are measured and recorded (104). Since the applied voltagewaveform is known, theoretically it should only be necessary to measurethe current signal at the transmit coil. However, in practice by thetime the applied voltage signal reaches the transmit coil it is likelyto be slightly different due to the effects of other elements in thetransfer circuit. Thus, to achieve a higher level of precision inmodeling the system, it is desirable to measure both the current signaland the voltage signal at the transmit coil.

After measuring the current and voltage signals, a known nonlinearsystem identification procedure, such as one of those referenced above,is used to fit an appropriate nonlinear system model (e.g. a Wienermodel) to the measured data to obtain an estimate of the linear andnonlinear waveforms characterizing the dynamic system model (e.g.impedance) for the measured system (106). In this case, the linearwaveform is the impulse response representing the dynamic linear portion(or the transfer function), and the static nonlinear waveform could be abest-fit polynomial for the nonlinear static portion of the model.

In the described embodiment, the representation of the linear waveformis transformed into the frequency domain by using an appropriatetechnique (e.g. Fourier transform or FFT) for transforming the impulseresponse into the corresponding impedance spectrum (108). The computedimpedance spectrum along with the corresponding nonlinear waveform isstored in association with the particular configuration for which it wascomputed (110).

This sequence of data acquisition steps is repeated for all definedconfigurations to build up a database of dynamic system models of thetransmitter/receiver system. In other words, after storing the computedinformation for the just completed test, it is determined whether alllocations have been tested (112). If more locations remain to be tested,the receiver coil is moved to another location or orientation among thedefined locations/orientations (114), and the sequence of measurementsand computations is repeated for the new configuration.

The resulting database represents a space of impedance spectra andnonlinearities. That space is then parameterized by determining anoptimal basis for the two sets of stored waveforms. In other words, thisis done for all of the impedance waveforms (116) and for all of thenonlinearity waveforms (118). Any of a variety of known techniques canbe used to accomplish this. In the described embodiment, SVD (SingularValue Decomposition) is used. After computing the optimal sets of basisfunctions for the two sets of waveforms, a reduced set of basisfunctions is defined (120). This involves selecting the subset ofoptimal basis functions that is most effective in representing thewaveforms and eliminating those basis functions that have littleexplanatory power in terms of representing those waveforms. Stateddifferently, it involves identifying that subset of the set of basisfunctions that is sufficiently discriminating. Techniques foridentifying a reduced set are well known.

Using the systems that were employed to generate the data, an empiricaldetermination is also made to identify for which positions and/orconfigurations of the receiver coil wireless power transfer can besuccessfully initiated. These determinations provide a basis forpartitioning the model space to identify regions representing a detectedreceiver that is in position. Generally known classification methods areused to perform the partitioning or clustering of the model space todefine those regions representing a receiver that is within anacceptable distance for initiating wireless power transfer.

The result of running the process depicted in FIG. 9 is a set oftraining data including a reduced set of optimal basis functions. Thepower transmitter controllers use the training data, including the datadefining the dynamic system models in the model space and the optimalset of basis functions characterizing the model space, as well as thethreshold criteria for classification of that space, to determinewhether a receiver system is within range for initiating wirelesscharging. The training data is stored in local memory that is part ofand/or accessible to the power transmitter controllers.

The algorithm that is implemented by the processor system of thetransmitter power controller is shown in FIG. 10. The transmitter powercontroller, when activated to search for a receiver system within itsvicinity (200), initiates a search loop in which it repeatedly examinesthe impedance of its transmit coil to detect the presence of a receiversystem (202-216). Each time it enters this loop, it applies apseudo-random voltage signal to the transmit coil (202) and measures andrecords both the voltage signal and the current signal of the transmitcoil (204). It uses the same pseudo-random signal that was used togenerate the training data stored in the transmitter power controller.The controller then uses the previously utilized nonlinear systemidentification procedures to fit a Wiener model of the dynamic system(e.g. the impedance or admittance of the transmit coil) to that measureddata (206). This results in dynamic linear waveform and a staticnonlinear waveform representing the nonlinear model. Since the storedreference data was represented in the frequency domain as an impedancespectrum, as opposed to the time domain as an impulse response, thetransmitter controller transforms the dynamic linear waveform into thecorresponding impedance spectrum so that it can be compared against whatis stored in the database (208). At this point, the result of theprocessing is an impedance spectrum and nonlinear waveform.

Using the reduced set of basis functions that were derived for thereference data, the transmitter controller decomposes the impedancespectrum into its respective basis functions and decomposes thenonlinear representation into its respective basis functions (212). Theresult is a set of coefficients representing the weights given to thebasis functions to represent the waveforms.

Then, using the classification information previously computed for themodel space, the transmitter controller determines whether it hasdetected a receiver coil that is in position to commence wireless powertransfer (214). It can do this, for example, by comparing points inmodel space representing the current measurements to the computedregions representing tests objects using the empirically derivedthresholds.

If it is determined that a receiver coil has been detected within rangefor initiating wireless power transfer, the controller generates asignal causing the initiation of a wireless power transfer (216, 218).Otherwise, the transmitter controller repeats the just describeddetection loop (216, 202). The transmitter controller continues cyclingthrough the loop until it detects a receiver coil with which wirelesspower transfer can be performed.

The approach described in connection with FIGS. 9 and 10 employs anindirect method. As an indirect method, a linear or nonlinear model isfirst obtained. Then, the waveforms for these models are decomposedusing the appropriate basis functions. And only then can the parametersof the basis functions be used with a classification method to get ameasure of goodness of position of a receiver unit.

In contrast, nonlinear system identification methods can also be used toimplement direct methods to achieve the same goal, such as providing ameasure of how close a receiver unit is. Such a direct method takesadvantage of the fact that any nonlinear process that can be recast as adeterministic time invariant memory-less mapping of a domain of inputfunctions to a range of output functions can be represented by anonlinear system such as a Volterra series expansion or parallel cascadeof structured blocks, and that the systems can be identified from aninput function if enough information is contained in the input andoutput pseudo-signals. Further discussion of the principles underlyingthis approach can be found in Green et al., “Recognition of AdenosineTriphosphate Binding Sites Using Parallel Cascade SystemIdentification”, Annals of Biomedical Engineering, Vol. 31, pp. 462-470,2003 (referred to hereinafter as Green).

An embodiment that employs such an approach is illustrated in FIGS. 11and 12. In the illustrated embodiment, the impedance of a coil is usedto detect a receiver. In general, as shown in FIG. 11, a collection ofcell impedance spectra for different receiver coil positions iscollected. Then, assuming that each measured spectrum consists of Npoints, at the corresponding coil position a constant position signal iscreated which also consists of N points, and each point of the measuredspectrum is assigned the corresponding receiver coil position. Forsystem identification purposes, an input signal is created byconcatenating together the impedance spectra, and an output signal iscreated by concatenating together the position signals. Subsequently,using nonlinear system identification, a nonlinear model, such as aparallel cascade of structured blocks, is obtained for that input-outputsystem.

This model is then used, as shown in FIG. 12, as an estimator ofreceiver coil position. For this, an acquired spectrum for an unknowncoil position is obtained and used as input to the estimated nonlinearmodel. The computed output from the nonlinear model directly provides anestimate of the receiver coil position. As explained in Green, the lastvalue of the output signal is used as the estimated coil position.

The particulars of the algorithms shown in FIGS. 11 and 12 will now bedescribed in greater detail.

FIG. 11 shows the procedure for pre-training a transmitter controllerfor detecting a receiver coil. As previously described in connectionwith FIG. 12, the procedure begins with first defining a representativeset of system configurations for which tests will be performed (300).

Using this defined set of system configurations, data is gathered andprocessed for each of the defined configurations. This involves, foreach configuration, driving the transmit coil of the power transfercircuit with a pseudo-random voltage signal (e.g. GWN voltage signal)that includes sufficient power to stimulate the nonlinear elements inthe wireless receiver system (302). While power is being applied to thetransmit coil, the voltage and current at the transmit coil are measuredand recorded (304). After measuring the current and voltage signals, theimpedance spectrum is computed (306). In the described embodiment, thisis done using cross-correlations of the measured signals as follows:

${H(x)} = \frac{F\left\{ {C_{xy}(t)} \right\}}{F\left\{ {C_{xx}(t)} \right\}}$C_(xy)(τ) = ∫y(t)x(t − τ)τ C_(xx)(τ) = ∫x(t)x(t − τ)τ

where C_(xx)(τ) is the autocorrelation of the voltage signal, C_(xy)(τ)is the cross correlation of the voltage and current signals, and F{•}represents the Fast Fourier Transform (FFT). In this case, the resultingcomputed impedance spectrum is represented by N points of data.

This is, of course, not the only way to compute the impedance spectrum.Other approaches are well known to persons of ordinary skill in the art.For example, one could simply compute the Fourier transforms of themeasured signals and use that to compute the impedance spectrum.However, the use of the cross correlations has the advantage of reducingthe effects of the noise.

The computed impedance spectrum corresponds to a coil configurationwhich is represented by a position or location relative to the transmitcoil. That coil position is assigned a unique number (e.g. distance fromthe transmit coil) and a corresponding N-point coil position signal,with each point equal to the assigned number, is generated (30) andstored as an output signal along with its associated N-point impedancespectrum which for which it was computed (310).

This sequence of steps is performed for each of the definedconfigurations (314, 312). When completed, the result is a database ofstored computed spectra and corresponding coil position signals for alldefined configurations.

Once the measurement data for all of the configurations has beenacquired, the impedance spectra for all of the configurations areconcatenated together to form an input signal (316):

Input Signal={H₁,H₂, . . . ,H_(n)}.

And all of the corresponding position signals are concatenated togetherto form an output signal (318):

Output Signal={P₁,P₂, . . . ,P_(n)}.

These two signals are then treated as the input and output signals of ahypothetical nonlinear system. Nonlinear system identification is thenused to obtain a nonlinear model, such as a parallel cascade ofstructured blocks, of that nonlinear system (320). This nonlinear modelis stored in the transmitter power controller which will use it todirectly estimate the position of the receiver coil during operation. Inaddition, a determination is empirically made with regard to whatpositions represent positions at which wireless charging can beperformed. And that information is also stored for use by thetransmitter power controller.

The details of the algorithm implemented by the transmitter powercontroller are illustrated in FIG. 12. The transmitter power controller,when activated to search for a receiver system within its vicinity(400), initiates a search loop in which it repeatedly measures theimpedance of its transmit coil to detect the presence of a receiversystem 402-412). Each time it executes this loop, it applies apseudo-random voltage signal to the transmit coil (402) (optionallyusing the same pseudo-random signal that was used to generate the dataset stored in the transmitter power controller) and measures both thevoltage signal and the current signal of the transmit coil (404). Then,using the approach described in connection with FIG. 11, the controllercomputes from the measured voltage and current signals the N-pointimpedance spectrum for the transmit coil (406). Next, it applies thiscomputed impedance spectrum to the nonlinear model that was generated inconnection with FIG. 11 (408). The result is an indicator of theposition of the receiver coil (410). If the output of the nonlinearmodel indicates that the receive coil is in position, the power transmitcontroller starts wirelessly charging the receiver system (412, 414).Otherwise, it repeats the just described loop to continue searching fora receive coil that is in position for charging (412, 402).

The above-described approach used the computed impedance. An alternativeapproach, which is likely to preserve more information about thenonlinearities in the system, is to use the measured current instead ofcomputing the impedance. Such an approach is described later inconnection with FIGS. 18A-B which relate to foreign object detection.

Auto-Tuning the Transmitter Frequency

The techniques described for the determining coil position are adaptedto tune the power transfer circuit to an optimal frequency for wirelesscharging of the receiver system. When the coils are at a distancefarther than a coil diameter, typically little tuning is required, asthe optimal frequency for transmitting power does not changesignificantly and is known for that system. As noted above, this is notthe case when the coils get closer. The optimal frequency may shiftrapidly with change in position, as the coils get closer to each other.Thus, the optimal frequency must be computed at a speed directly relatedto coil velocities. In this case, techniques based on systemidentification offer a clear advantage in that the required impedancespectra can be determined much more rapidly. Rather than sweepingthrough frequencies, which can be a time-consuming process, a signalcontaining all the required frequency components is applied once, andfrom that the spectra is estimated.

The power going into the primary coil, which corresponds to thetransmitted power plus some power dissipated in the coil itself as heat,can be derived from the impedance spectra. If the coil transmissionsystem can be represented mostly by a linear system, the power goinginto the transmit coil is given from the integral of voltage andcurrent. From the impedance spectrum, we get:

${P(\omega)} = {\frac{1}{T}{\int_{0}^{T}{V_{0}*{\cos \left( {\omega*t} \right)}*{Z(\omega)}^{- 1}*V_{0}{\cos \left( {{\omega*t} - {\varphi (\omega)}} \right)}*{t}}}}$

where the Z(ω) is the magnitude of the impedance and φ(ω) is the phase.

${P(\omega)} = {\frac{1}{2}\frac{V_{0}^{2}}{Z(\omega)}*{\cos \left( {\varphi (\omega)} \right)}}$

Hence, in the case of a linear system, optimal power transfer will occurwhere the product of the amplitude and cosine of the phase of theimpedance is at a maximum. If the system is significantly nonlinear, theoptimal frequencies will also be amplitude dependent.

In the case of a wireless power transmission system (WPTS), low-levelvoltages may not be large enough to stimulate internal nonlinearelectronics involving semiconductor devices and thus the system willremain linear. It is therefore desired that power in excess of someminimal amount be delivered to the coil to observe the nonlinearities inthe system. The random excitation signal may be applied as a voltagewaveform to the coils, and the response is measured as the currentflowing through the coil.

Nonlinear system identification is used to model the system and usingthe resulting nonlinear model, the optimum frequency is estimated.Ideally, the estimation process would be performed analytically. Inother words, parametric closed-form equations would be used to representthe impulse response and nonlinearities, or higher order kernels. Withclosed-form analytical expressions, calculating the system's responsesas well as finding parameter values where extrema occur can be doneanalytically. For finding optimal frequencies, an analytical responsegiving the coil power as a function of frequency is derived forsinusoidal input voltage waveforms, differentiated to solve for zeros,and zeros that occur at maxima are selected.

However, the analytical representations may become too complex to handleor be solved. In that case, it is possible to resort to numericaltechniques. For quickly locating an optimal frequency, an optimizationtechnique along a line (frequency axis) can be implemented. Forreferences discussing such techniques refer to Fletcher, R. PracticalMethods of Optimization, Second Edition, John Wiley & Sons, LTD, 1987;Press, W. H. et al., Numerical Recipes in C++, Cambridge UniversityPress, 2002; Nocedal, J. and Wright, S. J., Numerical Optimization,Springer Series in Operations Research, 1999; and Press, William H. etal., Numerical Recipes 3rd Edition: The Art of Scientific Computing,Cambridge University Press, 3^(rd) Ed., 2007). To this end, thenonlinear system response is simulated numerically and the transmittedpower is used as the objective function that is maximized. The sameapproach could be used for semi-analytical representations, wherenon-parametric functions could be approximated by a Chebychef seriesexpansion.

An example of an algorithm employing such an approach to automaticallytune the coil frequency is presented in FIG. 13. This algorithm isimplemented by the transmitter power controller. In general, nonlinearmodels are continuously acquired by repeatedly applying pseudo-randomvoltage perturbations, sampling the coil voltage and current, andfitting a nonlinear model to the measured data. The nonlinear models arethen used to simulate digitally the power going into the coil as afunction of signal frequency. And a search algorithm is used to findfrequencies at which the power should be calculated and iterativelylocate the optimum frequency.

As indicated in FIG. 13, when a receiver coil is determined to be withina distance over which charging can successfully occur, the transmitterpower controller sets the wireless charging frequency to a predeterminedfrequency F₀ corresponding to the estimated resonant frequency of thecharging system and begins wirelessly charging at that frequency (500).The controller then enters a loop in which it searches for the optimumcharging frequency. To conduct the search for the optimum frequency, thetransmitter power controller uses nonlinear system identification toconstruct a nonlinear model of the transmitter-receiver system and thenuses that model to find the optimum frequency. The construction of themodel is done in a manner similar to what has already been described inconnection with the process for detecting the presence of a receivercoil. It applies a pseudo-random voltage signal as a perturbation signalto the transmit coil (502) and measures both the voltage and the currentsignals of the transmit coil (504). The controller then uses nonlinearsystem identification to fit that measured data to a Wiener model of adynamic system representation (e.g. the impedance) of the transmit coil(506). Once the Wiener model has been derived, the controller enters aloop in which it uses that nonlinear model to search for the optimumfrequency (508-516). More specifically, it simulates the nonlinearmodel's response to a voltage signal at the previously selectedfrequency (508). From the simulated response, it calculates transmittedpower as the objective function that is to be maximized (510). Then, ituses a known gradient minimization technique to find a new frequency atwhich the transmitted power is maximized (512). During this search forthe optimum frequency, the controller uses the nonlinear model torepeatedly simulate a response, each time changing the values of thefrequency of for drive signal until an apparent optimum is found. Afterfinding the apparent optimum frequency, it tests whether that newfrequency is indeed an optimum (514).

If it is determined that an optimum frequency has not yet been found,the controller repeats the procedure to continue the search for theoptimum frequency (516). In other words, it simulates the nonlinearmodel's response to the new frequency (508), calculates the objectivefunction at that new frequency (510), and uses the gradient minimizationtechnique to find a new frequency at which the objective function ismaximized (512). The controller repeats this process until the optimumis found, at which point it sets the frequency of the transmit coil tothis optimum frequency (518).

After setting the drive signal to the computed optimum frequency, thecontroller tests whether charging is complete (520). It can determinethis through detecting an abrupt change in the impedance of the receivercoil when the receiver system terminates or switches off the charging ofthe battery module. Alternatively, if there is a communication channelback to the transmitter system, the receiver system can send a signalover that channel to inform the transmitter system that the chargingfunction has ended. When the controller detects that charging iscomplete (520), it turns off the power thereby terminating wirelesspower transfer to the receiver coil system (522).

If charging is not complete, it is possible that the relative positionsof the transmit and receiver coils might have changed thereby affectingthe value of the optimum frequency, so the search for a new optimumfrequency is resumed. In other words, it branches back to the beginningof the algorithm (502) to continue the search.

Up to this point, it has been assumed that the wireless receiver systemdoes not communicate data on transmitted power back to the wirelesstransmitter system. If, however, data can be transmitted from thereceiver system to the transmission system, a nonlinear model can bederived between a voltage applied to the coil V(t) and power P(t) goingdirectly into an energy storage unit, such as a battery pack. Thenonlinear model is then used as described previously to locate the exactfrequency at which the maximum amount of power goes directly in anenergy storage sub-system, rather than in the coils, which includespower losses in the coils and power electronics. In addition, theefficiency can then also be calculated, allowing one to choose between amaximum charging rate and a maximally efficient charging rate.

As an alternative and more efficient method to auto-adjust to theoptimum excitation frequency, a direct method based on theabove-described Green's method can be used. In that case, a detectionalgorithm, in the form of a nonlinear model, is first obtained offline.That is done by carrying out a number of experiments on a real systemwith the transmit and receiver coils at different locations andorientations relative to each other. For each location/orientation, anoptimum coil frequency is determined and the corresponding coil currentis recorded. Given that this process is carried out offline, the optimalfrequency for each condition can be obtained either numerically orexperimentally or both.

After all the tests have been carried out and from the results of thosetests, both an input signal and an output signal are constructed for thepurpose of nonlinear system identification. The input signal is createdby concatenating all of the measured current signal responses to therandom excitation signal. And the output signal is created byconcatenating the optimum frequency values corresponding to thoseconcatenated current signals. After that, nonlinear systemidentification is carried out to derive from those constructed input andoutput signals an optimal nonlinear frequency estimator which is anonlinear model representing how the different response signals map intothe corresponding optimal frequencies.

Once the optimal nonlinear frequency estimator has been obtained, it isused in real-time by the power transmitter controller to extractdirectly from a response signal an optimal excitation frequency to use.This is done by applying a pseudo-random excitation signal, which isusually the same as the pseudo-random signal used to construct theestimator, and by using the response signal resulting from thatexcitation signal as an input to the nonlinear frequency estimator. Theoutput of the estimator identifies the optimal frequency for wirelesspower transfer under the existing conditions.

A more detailed illustration of this direct method is shown in FIGS. 14Aand 14B. There is an offline part (FIG. 14A) and a real-time part (FIG.14B). The offline part is conducted using equipment that is identical tothe equipment with which the real-time part will be implemented. It isfor the purpose of constructing a nonlinear model that can be used asthe estimator.

Referring now to 14A, for the offline part, a determination is made ofthe range conditions to be modeled (600). The range of conditionsreflects the different locations and orientations of the coils relativeto each other for which auto-tuning will be performed in real-time.Experiments are to be carried out offline for each of these differentconditions. Experiments can also be carried out with different objectsthat might be present in the energy field and that might affect thewireless power transfer. This could include, for example, shieldingmaterials, such as metal sheets or metal tubing, at different distancesfrom the coils. In other words, the objective would be to reproduce thereal life conditions under which wireless power transfer is likely to beused. Once the range of possible conditions is defined, the offlineprocess involves performing a sequence of tests for each of thedifferent conditions (602).

First, an optimal coil frequency to maximize power transfer isdetermined (604). Since this is done offline, it can be done eithernumerically or experimentally. Then, a pseudo random voltageperturbation signal is applied to the transmit coil and the coil currentfor the voltage signal is measured and recorded (606). A constantfrequency signal with a value equal to the corresponding optimumfrequency is also defined and stored in association with the recordedcurrent signal (608).

That sequence of steps is repeated for all of the conditions that are tobe modeled (610).

Once the measurement data for all of the conditions has been acquired,input and output signals for a nonlinear model are constructed in amanner that is similar to what was previously described in connectionwith FIG. 11. The recorded current signals for all of the conditions areconcatenated together to form an input signal (612). And all of thecorresponding frequency signals are concatenated together to form anoutput signal (614). The concatenated input and output signals are thentreated as the input and output signals of a nonlinear system andnonlinear system identification is used to obtain a nonlinear model,such as a parallel cascade of structured blocks, of that nonlinearsystem (616). The resulting nonlinear model, which represents thefrequency estimator, will be used in real-time by the transmitter powercontroller to determine the optimum frequency for wireless powertransfer.

The algorithm illustrated in FIG. 14B represents the sequence of stepsthat are executed by the transmitter power controller. The transmitterpower controller applies the pseudo-random voltage signal to thetransmit coil and records the current signal produced by the transmitcoil (618). Then, it uses the recorded current signal as an input to thenonlinear frequency estimator that was computed offline (620). The finaloutput of the estimator is the optimum frequency for conducting powertransfer under the existing conditions. i.e., the existing position andorientation of the two coils with respect to each other. The controllersets the frequency of the power transfer circuit to that frequency(622). At this point it checks whether the charging is complete (624).If it is, the power is turned off and wireless power transfer isterminated or a flag is set which causes power transfer to terminate ornot take place (626).

If charging is not complete, the controller repeats the just-describedsequence of steps 618-624 to account for any possible changes in theposition of the transmit coil relative to the receiver coil or otheranticipated changes in conditions. In other words, the auto-tuningprocess runs continually throughout the wireless power transferoperation.

Note that the algorithm just described involved charging while thepseudo-random signal is being applied to the system. Another approach,which would reduce the noise in the output signal, would be tointerleave the charging with the testing. In that case, thepseudo-random signal would be applied while no drive signal is beingapplied.

Adjusting the Transmitter Waveform for Optimal Transfer

In theory, voltage and current signals flowing through self-resonantcoils are typically sinusoidal, given that the physics of oscillators issuch that they filter out most other waveforms. However, it is found inpractice that higher power transfer efficiency can sometimes be obtainedby using a slightly different waveform. In view of the fact that modernelectronics mostly uses digitally controlled switching systems to createpower signals, and not analog oscillators, the electronics lends itselfto creating almost any arbitrary waveform.

Adjusting the waveform becomes possible after a nonlinear modeldescribing the wireless power transmission is obtained. With thenonlinear model in hand, it is possible to employ an existing nonlinearcontrol algorithm to obtain an improved waveform. Given that no generalsolution exists, the system dynamics are linearized around a trajectoryin the state-space. Iteratively, and using existing linear controltheory, the optimal control waveform is re-calculated and the system isre-linearized around the new trajectory until convergence is achieved.

Alternatively, a numerical method can be used to calculate an optimalvoltage waveform. Such a method is depicted in FIG. 15. In thisflowchart the waveform is represented as finite series, such as atruncated Fourier series or sum of Chebychef polynomials. For atruncated Fourier series, the voltage can be represented as:

${V(t)} = {\sum\limits_{k = 0}^{N}{C_{k}*{\cos \left( {{k*\omega_{0}*t} + \varphi_{k}} \right)}}}$

where ω₀ is the fundamental angular frequency. For an optimum to exist,some constraints must be specified, either as bounds on V(t) or wherethe signal power P₀ is fixed and phase of the fundamental assumed to bezero:

$P_{0} = {\sum\limits_{k = 0}^{N}C_{k}^{2}}$ φ₀ = 0

A numerical optimization technique, such as a Levenberg-Marquardtoptimization procedure with linear and nonlinear constraints, is thenused to find the unknown parameters C_(k) and φ_(k).

If it is found that the optimal waveform needs to be adjusted with thereceiver coil position or as the receiver energy storage level changes,the same technique can be used iteratively in real time as the receivermoves.

FIG. 15 presents details of an exemplary algorithm for finding anoptimal waveform for power transfer. In general, this procedure involvesfirst acquiring a nonlinear model (700). Then, using that nonlinearmodel to simulate the power in the coil, an optimization technique isemployed to locate the best waveform.

The nonlinear model for the transmitter and receiver system may beacquired as described previously. Although this can be done inreal-time, when using pre-defined wireless charging systems it isgenerally not the case that the optimal waveform will vary significantlybetween different embodiments of the system. So, determining the optimalsignal waveform can be done offline before the charging process

After the nonlinear model has been acquired, the parameters for thefinite series representation of the voltage waveform, which wasdiscussed above, are initialized so that the waveform is a pure sinusoid(702). That is, C₀ is set to equal V₀, the amplitude of the appliedvoltage signal and the phase φ₀ is set to zero, as are all of the otherparameters, C_(k) and φ_(k). In addition, the various parameters for theoptimization algorithm that is be used are also initialized inpreparation for beginning the optimization search (704).

Once initialization is complete, the processor system on which this isbeing run begins executing the optimization algorithm (706-718). Thisinvolves a sequence of steps that are repeated until it finds a set ofparameter values, C_(k) and φ_(k), which maximizes the objectivefunction, namely, the output power of the transmit coil. The processoruses the acquired nonlinear model to simulate the current waveform thatresults from driving the system with the selected voltage waveform(706). It then computes the output power to the transmit coil and setsthe objective function equal to that computed output power (708). Next,the processor invokes a known nonlinear minimization procedure to find aset of parameter values that maximizes the output power (712). Duringthis search for the optimum waveform, the controller uses the nonlinearmodel to repeatedly simulate the current waveform, each time changingthe values of the parameters, C_(k) and φ_(k), for drive signal until anapparent optimum is found.

After finding the set of parameter values that maximizes the outputpower, the processor updates the voltage signal with those newparameters (714) and tests whether an optimum has been found (716). Ifit is determined that an optimum has not been found, the processorsystem branches back to the beginning of this optimization loop andrepeats the calculations to find a better set of values (718).

Once the optimum set of values is found, the processor saves thosevalues for use by the transmitter power controller (720).

If an adequate communication channel exists between the receiver andtransmitter and it is possible to get the actual power signal going tothe energy storage pack, the algorithm can be adapted to directlypredict the stored power and then adjust the input waveform to optimizethe stored power.

Detecting the Presence of Foreign Objects

The detection of foreign objects near the power coils is required bothfor safety and efficiencies. Given the relatively low frequencies usedin transmitting power and given that power transmission is achieved byinductive coupling with little electromagnetic radiation generated bythe coils, RIC is inherently safer than most other wireless powertransmission methods, particularly those using microwaves or light.

Objects that do not interfere with the magnetic field would rarely be ofany concern from a safety viewpoint or interference with the wirelesscharging system. If such objects need to be detected, other means, suchas optical, mechanical, or acoustic methods have to be used.

Of greater concern are objects that are electrically conductive, such aspieces of metal, carbon fiber material, or even living tissue. If suchan object is present, strong eddy currents could be generated causingtransmission losses, local heating in the object, and potentiallyleading to degradation or even worse causing cell damage in humans. Whenhundreds of kilowatts are transmitted, Joule heating could elevate thetemperature of foreign objects above 50° C. and result in skin burns.Upon reaching hundreds of kilowatts, exposure risks would exist for longexposure and possibly for people wearing pacemakers. So, it is importantto detect when conductive objects, including living tissue, comes intoproximity of the transmit coil.

Proximity sensors and optical techniques can be used. Methods have beenproposed for detecting the presence of foreign objects based on modelsfor determining unexpected power losses. See, for example, Kuyvenhoven,N., Dean, C., Melton, J., Schwannecke, J., and Umenei, A. E.,“Development of a Foreign Object Detection and Analysis Method forWireless Power Systems,” IEEE, Wireless Power Consortium (2011). Whenthe receiver coil sub-system can communicate with the transmittersub-system, it can inform the transmitter of the amount of powerreceived. The controller in the transmitter sub-system can calculatelosses from the known amount of energy going to the coils. And if theamount of unaccounted power exceeds a predefined threshold, the powertransmission would be interrupted.

However, detecting such objects directly from the modifications theycause in the fields sensed by coils is much more practical. As describedherein, by continuously estimating the nonlinear dynamic response of thetransmit coil, it is possible to detect a foreign object from the mannerin which it alters the transmitter dynamics. Nonlinearities tend tooccur primarily in the receiver electronics system. Nonlinearities areless likely to occur in metallic or other electrically conductivesystems, such as biological tissues, which do not exhibit saturation.

In addition, typical electrical components react linearly in thepresence of an electric field, and, in the frequency domain, the bodeplots of their impedance are defined by straight lines with integerslopes in a log-log plot. Also, the phase is linearly related to theslope of the impedance versus frequency and they behave as minimal phasesystems. In the case of living tissue, typically the slope is not anintegral number and it does not respond as a minimal phase system. So,various foreign objects, particularly in the case of complex structuresor large distributed masses, will primarily affect the linear part of anonlinear system in a very characteristic way.

A detection system in the transmitter power controller can be trainedwith different types of materials so that it can detect, based onchanges in the linear and nonlinear components of the coil system, thata foreign object has come into the neighborhood of the transmit coil.First, a large collection of nonlinear models is identified and storedin a database as representative of typical objects, metal or artificialtissues, to be detected. Subsequently, these models are classified usingexisting mathematical techniques such as neural networks, PrincipalComponent Analysis (PCA), or SVD. PCA and SVD can be used to define anoptimal basis to represent the models from which a highly reducedrepresentation of the model space is obtained and used for detection.

Given that different objects affect the linear and/or nonlinearcomponents of nonlinear models differently, their unique characteristicscan be isolated in different components of the optimal basis functions.Therefore, if an object, such as human body, comes into the neighborhoodof a wireless transmission system, its specific components will appearin the coefficients related to its particular basis functions and becomedetectable from the coefficient values. In such a case, it is sufficientto look at the coefficients characteristic of the human or animal modesto determine if a human or animal is in proximity to the coil.Alternatively, the space can be partitioned into regions thatcharacterize each object of interest. Subsequently, when a model movestowards or outside the boundaries of its cluster space, it gives anindication of the presence of foreign objects.

FIGS. 16A and 16B illustrate an implementation of a training algorithmfor one such system. In general, nonlinear system identification is usedto collect a large number of dynamic system models for different typesof objects at different locations relative to the transmit coil. Afterdata collection for those objects, optimal basis functions are derivedusing SVD decomposition. Subsequently, modes that are mostlyrepresentative of each type of object are isolated in the model space bylocating which modes have significant components that are unique to thatobject type.

FIG. 17 indicates how the detection proceeds in the field using theinformation gathered from the training algorithm. In general, raw datais continuously acquired and nonlinear models continuously derived fromthat raw data. Each time a new model is acquired, it is decomposed intoits optimal basis coefficients. Then, for each representative type ofobject of interest in the database, when corresponding mode coefficientsexceed a predefined threshold, that signals the likely presence of thattype of object and wireless power transfer is interrupted or a warningsignal is issued.

A more detailed description of the embodiments illustrated by FIGS. 16A,16B and 17 now follows.

Referring to FIG. 16A, constructing a database (or pre-training) forforeign object detection involves a set of steps that are similar tothose performed for detecting the presence of a receiver, as illustratedby FIG. 9. It begins by identifying and assembling a collection ofobjects that one wishes to detect (800). So, for example, since it isdesired that the charging process be terminated when a human or animalcomes into the wireless power transfer field, objects representative ofhuman and animal tissue need to be included in the collection ofobjects. Since it is also desirable to detect the presence of objectsthat either interfere with the wireless transfer of power and/or will bedamaged or cause potential damage to people if present in the wirelesspower transfer field, those objects also need to be identified and addedto the collection of objects.

Once the collection of objects has been identified, a set of positionsfor those objects relative to the transmit coil is defined (802). Thisis a representative set of positions at which it is desired to detectthe presence of any of the objects in the power transfer field.

With the collection of objects identified and the set of positionsdefined, the training procedure involves performing experiments on eachof the objects (804) at each of those positions (806) to derive acorresponding nonlinear model of the system. For a selected objectlocated at one of the defined positions, a pseudo-random voltage signal(e.g. GWN signal) is applied to the transmit coil (808). While thatpseudo-random signal is being applied to the transmit coil, the voltagesignal and the current signal of the transmit coil is recorded and therecorded signals are fit to a Wiener model of the impedance of thetransmit coil (810). The result is a linear part represented by animpulse response for the impedance and a nonlinear part represented by anonlinearity waveform. Both the impulse response and the nonlinearitywaveform are stored in association with the selected object and thatselected position (812).

This process is repeated for each object (814) and at each of thedefined positions (816) until all of the objects have been tested at allof the defined positions.

After collecting the data for each object at each of the definedpositions, and using well-known techniques, an optimal basis for thecollection of stored impedance waveforms is computed (818) and anoptimal basis for the stored collection of nonlinearity waveforms iscomputed (820). This process employs an appropriate one of thewell-known decomposition technique, such as SVD or PCA, etc. From thetwo optimal bases that have been computed, a reduced set of basisfunctions is identified (822).

Referring now to FIG. 16B, as part of the training procedure, anotherset of steps is executed for each object of the collection (824). Foreach position of an object selected from that collection of objects, thepreviously computed nonlinear model for that object at that position(826) is decomposed into the reduced basis functions that wereidentified during the first part of the training algorithm (828). Whenthe processor system has performed this task for each position for whichdata was recorded for that object (830), it then determines the basisfunction modes that are particular to that object for that collection ofmodels (832). In other words, it identifies the subset of basisfunctions that are most relevant to the computed set of data for thatobject. The coefficients of those basis functions define a point in themodel space for the corresponding model. For the collection of suchpoints for all the models for that object representing the differentpositions/orientations it is possible to use a well-known classificationtechnique to define a region of that space that corresponds to thecorresponding object.

The just-described process is performed for each of the objects in thecollection until they have all been analyzed (834). The results definethe regions of model space that correspond to the detected presence of acorresponding one of the foreign objects. It is the reduced set of basisfunctions along with the classification or cluster information that isstored in the transmitter power controller to be used during objectdetection. This data is stored in memory in the wireless powertransmitter system for use by the detection algorithm.

Referring to FIG. 17, to perform object detection, the transmitter powercontroller applies a pseudo-random voltage signal (e.g. GWN) to thetransmit coil (900) and it measures and records the voltage and currentwaveforms at the coil (902). Then, using nonlinear system identificationas previously described, the controller fits a nonlinear dynamic systemmodel for the coil (e.g. impedance) to the data for the recordedwaveforms (904). Next, using the reduced set of basis functionsidentified during the training phase, the controller decomposes thewaveforms obtained for the nonlinear model into those basis functions toobtain a vector representing the set of coefficients for the basisfunctions (906).

For a selected foreign object represented in its database (908), thecontroller then determines the coefficients of the basis functions thatwere determined during training to be particular to that object (910).Using those coefficients, it then determines where that vector falls inthe model space with respect to the region that had been previouslydetermined through classification methods to represent the foreignobject (912). If it is determined that the set of coefficients defines apoint in the model space that is within or close enough to the region(e.g. object size cluster), then it is concluded that a nearby foreignobject has been detected and the controller sets a flag thereby causingthe charging to terminate and/or not take place (914).

If it is determined that the object has not been detected, thecontroller clears any flag that might have been set to interruptcharging and moves on to test for the next object in its database (916).This process is repeated for each object in the database until all havebeen tested for (916).

After tests have been run for all objects in the database, thecontroller branches back to the beginning, applies another pseudo-randomsignal to the transmit coil, and conducts a new search for the presenceof any foreign objects.

A more direct and computationally more efficient alternative method isshown in FIGS. 18A and 18B. It employs the Green approach such as wasdescribed in connection with the auto-tuning algorithm depicted in FIGS.14A and 14B. The details are as follows.

As previously described, the process begins by identifying andassembling a collection of objects of a particular class that one wishesto detect (1000). In addition, for each object in the collection, allpositions that are to be tested are defined, as are the identity andpositions of any other objects that one might expect to find in theneighborhood of that object. One important classifier might be anythinglike biological tissue in the vicinity of the power coil. Anotherclassifier might be the presence of metal where eddy currents areinduced and result in power losses. The embodiment of FIGS. 18A-Billustrates a process for detecting biological tissue.

In general, to assemble a nonlinear model for detecting biologicaltissues, the various types and masses of biological tissue are placed atdifferent locations relative to the transmit coil and measurements aremade. More specifically, for each object and each position (1002), thefollowing steps are performed. A pseudo-random voltage perturbationsignal is applied to the transmit coil and, while doing so, theresulting current at the transmit coil is measured and recorded (1006).In addition, a safety signal is created for that object/positionindicating whether the field is considered to be hazardous or produce anunsafe condition (1008). This can be determined empirically during thisphase by using, for example, calibrated pickup coils. If it is felt thatthe object is in a danger zone (i.e., possible deleterious affects orunsafe conditions will result from being at that location), the value ofthe safety signal is set to a constant value (e.g. −1). Whereas, if itis felt that the object is in a safe zone (i.e., no deleterious affectsor unsafe conditions will result from being at that location), the valueof the safety signal is set to a different constant value (e.g. +1).This sequence of steps is repeated until it has been completed for allobjects at all defined positions (1010).

After all the data has been collected, all of the recorded currentsignals are concatenated together to create an input signal (1012) to beused for system identification purposes and all of the safety signalsare concatenated together in a similar manner to create a correspondingoutput signal (1014). Then, nonlinear system identification is used toderive a nonlinear model for a system characterized by those input andoutput signals. The resulting nonlinear model is used by the powertransmitter controller as a nonlinear filter that outputs a signalindicting whether a foreign object is present.

The operation of the power transmitter controller is shown in FIG. 18B.Subsequently, in the unknown environment, the power transmittercontroller initiates a search for foreign objects (1018). This involvesapplying a pseudo-random voltage signal to the transmit coil (the sameas was used to build the database of information about foreign objects),and measuring and recording the current waveform (1020). The recordedcurrent waveform is processed by the previously derived nonlinear system(1022). An output having a positive value indicates the possiblepresence of a foreign object (e.g. living tissue) and the powercontroller generates an interrupt signal that causes the power to thetransmit coil to be interrupted (1024). And the controller repeats thesearch loop to determine when the foreign object is no longer present

If the nonlinear filter outputs a negative value, indicating that noforeign object was found, any previously established interruption signalis cleared and the search loop is repeated.

The two specific examples of pseudo-random signals mentioned above wereGaussian White Noise (GWN) and Pseudo-Random Binary Sequences (PRBS).That is, however, not meant to be limiting. The reference topseudo-random is also meant to include, for example, noise-like signalsin which power may be concentrated so as to excite certain modes in thetarget system. In addition, it is meant to cover any signal that can beused to derive an impulse response or a transfer function for thestimulated system.

Other embodiments are within the scope of the following claims. Forexample, there are a number of “spaces” used to represent linear dynamicsystems parametrically: state-space methods; transfer functions;frequency functions; difference equations in the form of ARX, ARMA,ARMAX models; Z-domain polynomials; and time domain methods where theimpulse response is represented as a parametric function of time. Thereare methods to map representations between one space to another space,such as transfer functions to state-space representation. In theembodiments described above, time domain and frequency domainrepresentations were explicitly mentioned. However, that was notintended to be limiting. The use of any one of the alternativerepresentations is intended to fall within the scope of the invention.

In the above-described embodiments the dynamic system representationsthat were modeled were the transmit coil impedance and the transmit coilcurrent. One could, of course, model other dynamic systemsrepresentations including, for example, power transferred, power loss,and power efficiency. For some representations, it would be necessary toobtain information from the receiver through another channel. Forexample, power loss or power efficiency requires being able to monitorthe power that is supplied to the battery module; and thus would requirerelying on the receiver system to supply that information.

Although the above-described embodiments made specific reference to avehicle platform, it is not intended that the use of the inventionsdescribed herein be limited to only that platform. The inventionsdescribed herein have applicability to any wireless power transfersystem in which a wireless power transmitter and a receiver system mightbe brought into proximity of each other for the purpose of transferringpower from one system to the other.

In the case of the detection algorithms, it was indicated that thetransmit coil was used to detect the presence of a receiver coil or aforeign object. That need not be the case. One could instead use anadditional, separate coil dedicated to use by the detection function.

It should be further noted that the algorithms presented herein need notbe run only on a single processor, as might appear to have been implied.Multiple processors could be used with the tasks divided among them inan appropriate way. For example, in the case of the algorithm depictedin FIG. 9, it might be considered appropriate to perform all of the datacollection on one processor and to perform the nonlinear systemidentification of another processor.

1. A method of detecting whether a receiver coil is near a transmit coil in a wireless power transfer system (WPTS), said method comprising: applying a pseudo-random signal to the transmit coil; while the pseudo-random signal is being applied to the transmit coil, recording one or more signals produced within the WPTS in response to the applied pseudo-random signal; by using the one or more recorded signals, generating a dynamic system model for some aspect of the WPTS; and using the generated dynamic system model in combination with stored training data to determine whether an object having characteristics distinguishing the object as a receiver coil is near the transmit coil.
 2. The method of claim 1, further comprising, if the receiver coil is determined to be near the transmit coil, initiating a wireless power transfer through the transmit coil to the receiver coil.
 3. The method of claim 1, wherein the pseudo-random signal is a pseudo-random voltage signal.
 4. The method of claim 1, wherein the pseudo-random signal is sufficiently strong to stimulate nonlinearities in a receiver system connected to the receiver coil.
 5. The method of claim 3, wherein the one or more signals includes a current signal of the transmit coil.
 6. The method of claim 3, wherein the one or more signals includes a current signal and a voltage signal of the transmit coil.
 7. The method of claim 1, wherein using the generated dynamic system model comprises comparing information contained in the generated dynamic system model to empirically-derived, stored information that is indicative of a nearby presence of a receiver coil.
 8. The method of claim 1, wherein generating the dynamic system model comprises using system identification to fit a selected model to data derived from the one or more recorded signals.
 9. The method of claim 1, wherein generating the dynamic system model comprises using nonlinear system identification to fit a selected model to the data derived from the one or more recorded signals.
 10. The method of claim 9, wherein the selected model is a Wiener system.
 11. The method of claim 9, wherein the selected model is a Hammerstein system.
 12. The method of claim 9, wherein the selected model is a parallel cascade of structures blocks.
 13. The method of claim 9, wherein the selected model is a Volterra series.
 14. The method of claim 9, wherein the selected model is a parametric linear model.
 15. The method of claim 9, wherein the selected model has a dynamic linear part and a static nonlinear part.
 16. The method of claim 5, wherein the dynamic system model is an impedance function for the transmit coil.
 17. The method of claim 6, wherein the dynamic system model is a transmitted power function for transmit coil.
 18. The method of claim 1, wherein using the generated dynamic system model comprises decomposing the dynamic system model into basis functions to generate a set of basis function parameters.
 19. The method of claim 18, wherein using the generated dynamic system model further comprises using the set of basis function parameters to determine whether a receiver coil is near the transmit coil.
 20. The method of claim 1, wherein the pseudo-random signal is a selected one of a Gaussian White Noise signal and a Pseudo-Random Binary Sequence (PRBS).
 21. The method of claim 9, wherein the generated dynamic system model comprises a time domain representation.
 22. The method of claim 9, wherein the generated dynamic system model comprises a frequency domain representation.
 23. The method of claim 1, wherein the stored training data is represented by a stored filter function and wherein using the generated dynamic system model in combination with stored training data comprises processing the generated dynamic system model to generate an output signal, wherein the output signal indicates whether an object having characteristics recognizable from the stored training data as a receiver coil is near the transmit coil and wherein processing the dynamic system model comprises applying the stored filter function.
 24. The method of claim 23, wherein the one or more recorded signals includes a current signal of the transmit coil.
 25. The method of claim 23, wherein the generated dynamic system model is the measured current signal.
 26. The method of claim 23, wherein the filter function is a nonlinear filter function.
 27. The method of claim 26, wherein the nonlinear filter function was derived from measurements made on a test system including a test transmit coil and a test receiver coil located at different distances of separation from each other.
 28. The method of claim 23, further comprising, if a receiver coil is detected near the transmit coil, initiating a wireless power transfer through the transmit coil to the detected receiver coil.
 29. A wireless power transfer system comprising: a transmit coil; a power transmitter circuit connected to the transmit coil; a sensor circuit connected to the transmit coil; and a controller for controlling the power transmitter circuit and the sensor circuit, wherein said controller includes a memory for storing training data and a processor system programmed to: cause the power transmitter circuit to apply a pseudo-random signal to the transmit coil; while the pseudo-random signal is being applied to the transmit coil, cause the sensor circuit to record one or more signals produced within the WPTS in response to the applied pseudo-random signal; by using the one or more recorded signals, generate a dynamic system model for some aspect of the WPTS; and use the generated dynamic system model in combination with the stored training data to determine whether an object having characteristics distinguishing the object as a receiver coil is near the transmit coil.
 30. The wireless power transfer system of claim 29, wherein the one or more signals includes a current signal and a voltage signal of the transmit coil.
 31. The wireless power transfer system of claim 29, wherein the stored training data is represented by a stored filter function and wherein the processor system is programmed to use the generated dynamic system model in combination with stored training data by processing the generated dynamic system model with the filter function to generate an output signal, wherein the output signal indicates whether an object having characteristics recognizable from the stored training data as a receiver coil is near the transmit coil.
 32. The wireless power transfer system of claim 31, wherein the one or more recorded signals includes a current signal of the transmit coil.
 33. The wireless power transfer system of claim 31, wherein the generated dynamic system model is the measured current signal. 34-56. (canceled) 